.png){kind=icon}
返回
Hybrid quantum annealing decomposition framework for unit commitment
.png){kind=icon}
OA
AI
DOI：10.1016/j.epsr.2024.111121.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Quantum computing is an emerging and promising technology that has overwhelming quantum advantages compared to its classical counterparts. Unit commitment (UC) is a critical issue in the power system, and it becomes more challenging with the integration of intermittent renewable energy. Therefore, this paper proposes an innovative decomposition and coordination optimization framework to accelerate the solution of UC, in which the interaction between an adiabatic quantum computer and a classical computer is designed to harness the immense computational power of quantum computers effectively. First, decomposition methods considering the requirements of quantum computers are introduced to decompose UC into smallscale models. Then, the paper presents a quadratic unconstrained binary optimization modeling method to transform UC problems into the form of quantum computing. Furthermore, due to the limitations of quantum computing resources, a reductive variable technique is proposed to reduce the number of slack variables in the optimization model and ensure that it remains feasible for quantum computers. Case studies conducted in test systems with a quantum annealing simulator and a real quantum annealing computer illustrate the feasibility and effectiveness of the method and demonstrate its potential in the era of quantum computing.
Keyword:
Decomposition method
Quantum annealing
Mixed integer linear programming
Unit commitment
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
1.2W
被引数:
2.2W
机构
引用论文
分享收藏Effect of stress on the behavior and 5-HT system in Sprague-Dawley and Wistar Kyoto rat strains
PREAI
分享收藏Exertional rhabdomyolysis and renal failure in patients with sickle cell trait: Is it time to change our approach?镰状细胞性状患者的劳力性横纹肌溶解和肾衰竭: 是时候改变我们的方法了吗？.png)
Hematology
HematologyIF0
PREAI
分享收藏
查看更多内容